{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from scipy.stats import norm, skew\n",
    "from sklearn.preprocessing import LabelEncoder,StandardScaler,MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('Ames_House_train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 数据分布探索\n",
    "\n",
    "#### 1.1 查看缺失值情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LotFrontage      259\n",
      "Alley           1369\n",
      "MasVnrType         8\n",
      "MasVnrArea         8\n",
      "BsmtQual          37\n",
      "BsmtCond          37\n",
      "BsmtExposure      38\n",
      "BsmtFinType1      37\n",
      "BsmtFinType2      38\n",
      "Electrical         1\n",
      "FireplaceQu      690\n",
      "GarageType        81\n",
      "GarageYrBlt       81\n",
      "GarageFinish      81\n",
      "GarageQual        81\n",
      "GarageCond        81\n",
      "PoolQC          1453\n",
      "Fence           1179\n",
      "MiscFeature     1406\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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cAQAADCDuAAAABhB3AAAAA4g7AACAAcQdAADAAOIOAABgAHEHAAAwgLgDAAAYQNwBAAAM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\nGOD/A50op5A0Ydd9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203ff18dcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing = train_data.columns[train_data.isnull().any()].tolist()\n",
    "print(train_data[missing].isnull().sum())\n",
    "#训练数据的缺失值情况\n",
    "#LotFrontage      1201 non-null float64 房屋到街道的直线距离\n",
    "#Alley            91 non-null object 所在巷通道的类型 [nan 'Grvl' 'Pave']\n",
    "#MasVnrType       1452 non-null object  表层砌体（Masonry veneer）类型 ['BrkFace' 'None' 'Stone' 'BrkCmn' nan]\n",
    "#MasVnrArea       1452 non-null float64  表层砌体面积（square feet）\n",
    "#BsmtQual         1423 non-null object 地下室高度 ['Gd' 'TA' 'Ex' nan 'Fa']\n",
    "#BsmtCond         1423 non-null object 地下室的总体状况 ['TA' 'Gd' nan 'Fa' 'Po']\n",
    "#BsmtExposure     1422 non-null object  Walkout或花园层地下室的墙壁 ['No' 'Gd' 'Mn' 'Av' nan]\n",
    "#BsmtFinType1     1423 non-null object 完成的地下室的质量 ['GLQ' 'ALQ' 32wdsaz'Unf' 'Rec' 'BLQ' nan 'LwQ']\n",
    "#BsmtFinType2     1422 non-null object 第二类型完成面积(如果有) ['Unf' 'BLQ' nan 'ALQ' 'Rec' 'LwQ' 'GLQ']\n",
    "#Electrical       1459 non-null object 电气系统 ['SBrkr' 'FuseF' 'FuseA' 'FuseP' 'Mix' nan]\n",
    "#FireplaceQu      770 non-null object 壁炉质量 [nan 'TA' 'Gd' 'Fa' 'Ex' 'Po']\n",
    "#GarageType       1379 non-null object 车库位置 ['Attchd' 'Detchd' 'BuiltIn' 'CarPort' nan 'Basment' '2Types']\n",
    "#GarageYrBlt      1379 non-null float64 车库建造年份\n",
    "#GarageFinish     1379 non-null object I车库内部装修 ['RFn' 'Unf' 'Fin' nan]\n",
    "#GarageQual       1379 non-null object 车库质量 ['TA' 'Fa' 'Gd' nan 'Ex' 'Po']\n",
    "#GarageCond       1379 non-null object 车库条件 ['TA' 'Fa' nan 'Gd' 'Po' 'Ex']\n",
    "#PoolQC           7 non-null object  游泳池质量 [nan 'Ex' 'Fa' 'Gd']\n",
    "#Fence            281 non-null object 围栏质量 [nan 'MnPrv' 'GdWo' 'GdPrv' 'MnWw']\n",
    "#MiscFeature      54 non-null object 没被包含在其他类别的杂项功能 [nan 'Shed' 'Gar2' 'Othr' 'TenC']\n",
    "\n",
    "missing_data = train_data[missing].isnull().sum().sort_values(ascending=False)\n",
    "plt.subplots(figsize=(15,15))\n",
    "sns.barplot(missing_data.index,missing_data.values)\n",
    "plt.xlabel('Features', fontsize=15)\n",
    "plt.xticks(rotation='90')\n",
    "plt.title('Q', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 缺失值处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1 LotFrontage\n",
    "这个特征值缺失259个。从数据集上看，和LotArea似乎有些关系。先查看它们之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.426095018772\n"
     ]
    }
   ],
   "source": [
    "print(train_data['LotFrontage'].corr(train_data['LotArea']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.602002216794\n"
     ]
    }
   ],
   "source": [
    "train_data['LotArea_Sqr'] = np.sqrt(train_data['LotArea'])\n",
    "print(train_data['LotFrontage'].corr(train_data['LotArea_Sqr']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/AK/byXXnjqDQ5dTWbR7TgJtFEs33aq4397lcDvrZgbVfYQFAzIgwlZ804Oag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\nKELhAkCRB9Jn4Tu83v2mAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203ff075c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_data[['LotFrontage','LotArea_Sqr']].dropna())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
      "0        1          60       RL    65.000000     8450   Pave   NaN      Reg   \n",
      "1        2          20       RL    80.000000     9600   Pave   NaN      Reg   \n",
      "2        3          60       RL    68.000000    11250   Pave   NaN      IR1   \n",
      "3        4          70       RL    60.000000     9550   Pave   NaN      IR1   \n",
      "4        5          60       RL    84.000000    14260   Pave   NaN      IR1   \n",
      "5        6          50       RL    85.000000    14115   Pave   NaN      IR1   \n",
      "6        7          20       RL    75.000000    10084   Pave   NaN      Reg   \n",
      "7        8          60       RL   101.892100    10382   Pave   NaN      IR1   \n",
      "8        9          50       RM    51.000000     6120   Pave   NaN      Reg   \n",
      "9       10         190       RL    50.000000     7420   Pave   NaN      Reg   \n",
      "10      11          20       RL    70.000000    11200   Pave   NaN      Reg   \n",
      "11      12          60       RL    85.000000    11924   Pave   NaN      IR1   \n",
      "12      13          20       RL   113.877127    12968   Pave   NaN      IR2   \n",
      "13      14          20       RL    91.000000    10652   Pave   NaN      IR1   \n",
      "14      15          20       RL   104.498804    10920   Pave   NaN      IR1   \n",
      "15      16          45       RM    51.000000     6120   Pave   NaN      Reg   \n",
      "16      17          20       RL   106.023582    11241   Pave   NaN      IR1   \n",
      "17      18          90       RL    72.000000    10791   Pave   NaN      Reg   \n",
      "18      19          20       RL    66.000000    13695   Pave   NaN      Reg   \n",
      "19      20          20       RL    70.000000     7560   Pave   NaN      Reg   \n",
      "20      21          60       RL   101.000000    14215   Pave   NaN      IR1   \n",
      "21      22          45       RM    57.000000     7449   Pave  Grvl      Reg   \n",
      "22      23          20       RL    75.000000     9742   Pave   NaN      Reg   \n",
      "23      24         120       RM    44.000000     4224   Pave   NaN      Reg   \n",
      "24      25          20       RL    90.807489     8246   Pave   NaN      IR1   \n",
      "25      26          20       RL   110.000000    14230   Pave   NaN      Reg   \n",
      "26      27          20       RL    60.000000     7200   Pave   NaN      Reg   \n",
      "27      28          20       RL    98.000000    11478   Pave   NaN      Reg   \n",
      "28      29          20       RL    47.000000    16321   Pave   NaN      IR1   \n",
      "29      30          30       RM    60.000000     6324   Pave   NaN      IR1   \n",
      "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
      "1430  1431          60       RL    60.000000    21930   Pave   NaN      IR3   \n",
      "1431  1432         120       RL    70.199715     4928   Pave   NaN      IR1   \n",
      "1432  1433          30       RL    60.000000    10800   Pave  Grvl      Reg   \n",
      "1433  1434          60       RL    93.000000    10261   Pave   NaN      IR1   \n",
      "1434  1435          20       RL    80.000000    17400   Pave   NaN      Reg   \n",
      "1435  1436          20       RL    80.000000     8400   Pave   NaN      Reg   \n",
      "1436  1437          20       RL    60.000000     9000   Pave   NaN      Reg   \n",
      "1437  1438          20       RL    96.000000    12444   Pave   NaN      Reg   \n",
      "1438  1439          20       RM    90.000000     7407   Pave   NaN      Reg   \n",
      "1439  1440          60       RL    80.000000    11584   Pave   NaN      Reg   \n",
      "1440  1441          70       RL    79.000000    11526   Pave   NaN      IR1   \n",
      "1441  1442         120       RM    66.528190     4426   Pave   NaN      Reg   \n",
      "1442  1443          60       FV    85.000000    11003   Pave   NaN      Reg   \n",
      "1443  1444          30       RL    94.095696     8854   Pave   NaN      Reg   \n",
      "1444  1445          20       RL    63.000000     8500   Pave   NaN      Reg   \n",
      "1445  1446          85       RL    70.000000     8400   Pave   NaN      Reg   \n",
      "1446  1447          20       RL   161.684879    26142   Pave   NaN      IR1   \n",
      "1447  1448          60       RL    80.000000    10000   Pave   NaN      Reg   \n",
      "1448  1449          50       RL    70.000000    11767   Pave   NaN      Reg   \n",
      "1449  1450         180       RM    21.000000     1533   Pave   NaN      Reg   \n",
      "1450  1451          90       RL    60.000000     9000   Pave   NaN      Reg   \n",
      "1451  1452          20       RL    78.000000     9262   Pave   NaN      Reg   \n",
      "1452  1453         180       RM    35.000000     3675   Pave   NaN      Reg   \n",
      "1453  1454          20       RL    90.000000    17217   Pave   NaN      Reg   \n",
      "1454  1455          20       FV    62.000000     7500   Pave  Pave      Reg   \n",
      "1455  1456          60       RL    62.000000     7917   Pave   NaN      Reg   \n",
      "1456  1457          20       RL    85.000000    13175   Pave   NaN      Reg   \n",
      "1457  1458          70       RL    66.000000     9042   Pave   NaN      Reg   \n",
      "1458  1459          20       RL    68.000000     9717   Pave   NaN      Reg   \n",
      "1459  1460          20       RL    75.000000     9937   Pave   NaN      Reg   \n",
      "\n",
      "     LandContour Utilities    ...     PoolArea PoolQC  Fence MiscFeature  \\\n",
      "0            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "2            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "3            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "4            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "5            Lvl    AllPub    ...            0    NaN  MnPrv        Shed   \n",
      "6            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "7            Lvl    AllPub    ...            0    NaN    NaN        Shed   \n",
      "8            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "9            Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "10           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "11           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "12           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "13           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "14           Lvl    AllPub    ...            0    NaN   GdWo         NaN   \n",
      "15           Lvl    AllPub    ...            0    NaN  GdPrv         NaN   \n",
      "16           Lvl    AllPub    ...            0    NaN    NaN        Shed   \n",
      "17           Lvl    AllPub    ...            0    NaN    NaN        Shed   \n",
      "18           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "19           Lvl    AllPub    ...            0    NaN  MnPrv         NaN   \n",
      "20           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "21           Bnk    AllPub    ...            0    NaN  GdPrv         NaN   \n",
      "22           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "23           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "24           Lvl    AllPub    ...            0    NaN  MnPrv         NaN   \n",
      "25           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "26           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "27           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "28           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "29           Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "...          ...       ...    ...          ...    ...    ...         ...   \n",
      "1430         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1431         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1432         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1433         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1434         Low    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1435         Lvl    AllPub    ...            0    NaN  GdPrv         NaN   \n",
      "1436         Lvl    AllPub    ...            0    NaN   GdWo         NaN   \n",
      "1437         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1438         Lvl    AllPub    ...            0    NaN  MnPrv         NaN   \n",
      "1439         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1440         Bnk    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1441         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1442         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1443         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1444         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1445         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1446         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1447         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1448         Lvl    AllPub    ...            0    NaN   GdWo         NaN   \n",
      "1449         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1450         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1451         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1452         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1453         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1454         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1455         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1456         Lvl    AllPub    ...            0    NaN  MnPrv         NaN   \n",
      "1457         Lvl    AllPub    ...            0    NaN  GdPrv        Shed   \n",
      "1458         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "1459         Lvl    AllPub    ...            0    NaN    NaN         NaN   \n",
      "\n",
      "     MiscVal MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
      "0          0      2   2008        WD         Normal     208500  \n",
      "1          0      5   2007        WD         Normal     181500  \n",
      "2          0      9   2008        WD         Normal     223500  \n",
      "3          0      2   2006        WD        Abnorml     140000  \n",
      "4          0     12   2008        WD         Normal     250000  \n",
      "5        700     10   2009        WD         Normal     143000  \n",
      "6          0      8   2007        WD         Normal     307000  \n",
      "7        350     11   2009        WD         Normal     200000  \n",
      "8          0      4   2008        WD        Abnorml     129900  \n",
      "9          0      1   2008        WD         Normal     118000  \n",
      "10         0      2   2008        WD         Normal     129500  \n",
      "11         0      7   2006       New        Partial     345000  \n",
      "12         0      9   2008        WD         Normal     144000  \n",
      "13         0      8   2007       New        Partial     279500  \n",
      "14         0      5   2008        WD         Normal     157000  \n",
      "15         0      7   2007        WD         Normal     132000  \n",
      "16       700      3   2010        WD         Normal     149000  \n",
      "17       500     10   2006        WD         Normal      90000  \n",
      "18         0      6   2008        WD         Normal     159000  \n",
      "19         0      5   2009       COD        Abnorml     139000  \n",
      "20         0     11   2006       New        Partial     325300  \n",
      "21         0      6   2007        WD         Normal     139400  \n",
      "22         0      9   2008        WD         Normal     230000  \n",
      "23         0      6   2007        WD         Normal     129900  \n",
      "24         0      5   2010        WD         Normal     154000  \n",
      "25         0      7   2009        WD         Normal     256300  \n",
      "26         0      5   2010        WD         Normal     134800  \n",
      "27         0      5   2010        WD         Normal     306000  \n",
      "28         0     12   2006        WD         Normal     207500  \n",
      "29         0      5   2008        WD         Normal      68500  \n",
      "...      ...    ...    ...       ...            ...        ...  \n",
      "1430       0      7   2006        WD         Normal     192140  \n",
      "1431       0     10   2009        WD         Normal     143750  \n",
      "1432       0      8   2007        WD         Normal      64500  \n",
      "1433       0      5   2008        WD         Normal     186500  \n",
      "1434       0      5   2006        WD         Normal     160000  \n",
      "1435       0      7   2008       COD        Abnorml     174000  \n",
      "1436       0      5   2007        WD         Normal     120500  \n",
      "1437       0     11   2008       New        Partial     394617  \n",
      "1438       0      4   2010        WD         Normal     149700  \n",
      "1439       0     11   2007        WD         Normal     197000  \n",
      "1440       0      9   2008        WD         Normal     191000  \n",
      "1441       0      5   2008        WD         Normal     149300  \n",
      "1442       0      4   2009        WD         Normal     310000  \n",
      "1443       0      5   2009        WD         Normal     121000  \n",
      "1444       0     11   2007        WD         Normal     179600  \n",
      "1445       0      5   2007        WD         Normal     129000  \n",
      "1446       0      4   2010        WD         Normal     157900  \n",
      "1447       0     12   2007        WD         Normal     240000  \n",
      "1448       0      5   2007        WD         Normal     112000  \n",
      "1449       0      8   2006        WD        Abnorml      92000  \n",
      "1450       0      9   2009        WD         Normal     136000  \n",
      "1451       0      5   2009       New        Partial     287090  \n",
      "1452       0      5   2006        WD         Normal     145000  \n",
      "1453       0      7   2006        WD        Abnorml      84500  \n",
      "1454       0     10   2009        WD         Normal     185000  \n",
      "1455       0      8   2007        WD         Normal     175000  \n",
      "1456       0      2   2010        WD         Normal     210000  \n",
      "1457    2500      5   2010        WD         Normal     266500  \n",
      "1458       0      4   2010        WD         Normal     142125  \n",
      "1459       0      6   2008        WD         Normal     147500  \n",
      "\n",
      "[1460 rows x 81 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "cond = train_data['LotFrontage'].isnull()\n",
    "train_data.LotFrontage[cond]=train_data.LotArea_Sqr[cond]\n",
    "train_data.drop(['LotArea_Sqr'],axis = 1, inplace=True)\n",
    "print(train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2 Alley"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grvl    50\n",
      "Pave    41\n",
      "Name: Alley, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train_data['Alley'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#由于缺失值太多，猜测是没有这部分特征数据，所以赋值为None\n",
    "train_data.loc[train_data['Alley'].isnull(),'Alley'] = 'None'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None    1369\n",
      "Grvl      50\n",
      "Pave      41\n",
      "Name: Alley, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train_data['Alley'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3 MasVnrType和MasVnrArea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     MasVnrType  MasVnrArea\n",
      "234         NaN         NaN\n",
      "529         NaN         NaN\n",
      "650         NaN         NaN\n",
      "936         NaN         NaN\n",
      "973         NaN         NaN\n",
      "977         NaN         NaN\n",
      "1243        NaN         NaN\n",
      "1278        NaN         NaN\n"
     ]
    }
   ],
   "source": [
    "print(train_data[['MasVnrType','MasVnrArea']][train_data['MasVnrType'].isnull() == True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data.loc[train_data['MasVnrType'].isnull(),'MasVnrType'] = 'None'\n",
    "train_data.loc[train_data['MasVnrArea'].isnull(),'MasVnrArea'] = 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4 BsmtQual  BsmtCond BsmtExposure BsmtFinType1 BsmtFinType2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinType2  BsmtFinSF1  \\\n",
      "17        NaN      NaN          NaN          NaN          NaN           0   \n",
      "39        NaN      NaN          NaN          NaN          NaN           0   \n",
      "90        NaN      NaN          NaN          NaN          NaN           0   \n",
      "102       NaN      NaN          NaN          NaN          NaN           0   \n",
      "156       NaN      NaN          NaN          NaN          NaN           0   \n",
      "182       NaN      NaN          NaN          NaN          NaN           0   \n",
      "259       NaN      NaN          NaN          NaN          NaN           0   \n",
      "342       NaN      NaN          NaN          NaN          NaN           0   \n",
      "362       NaN      NaN          NaN          NaN          NaN           0   \n",
      "371       NaN      NaN          NaN          NaN          NaN           0   \n",
      "392       NaN      NaN          NaN          NaN          NaN           0   \n",
      "520       NaN      NaN          NaN          NaN          NaN           0   \n",
      "532       NaN      NaN          NaN          NaN          NaN           0   \n",
      "533       NaN      NaN          NaN          NaN          NaN           0   \n",
      "553       NaN      NaN          NaN          NaN          NaN           0   \n",
      "646       NaN      NaN          NaN          NaN          NaN           0   \n",
      "705       NaN      NaN          NaN          NaN          NaN           0   \n",
      "736       NaN      NaN          NaN          NaN          NaN           0   \n",
      "749       NaN      NaN          NaN          NaN          NaN           0   \n",
      "778       NaN      NaN          NaN          NaN          NaN           0   \n",
      "868       NaN      NaN          NaN          NaN          NaN           0   \n",
      "894       NaN      NaN          NaN          NaN          NaN           0   \n",
      "897       NaN      NaN          NaN          NaN          NaN           0   \n",
      "984       NaN      NaN          NaN          NaN          NaN           0   \n",
      "1000      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1011      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1035      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1045      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1048      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1049      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1090      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1179      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1216      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1218      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1232      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1321      NaN      NaN          NaN          NaN          NaN           0   \n",
      "1412      NaN      NaN          NaN          NaN          NaN           0   \n",
      "\n",
      "      BsmtFinSF2  \n",
      "17             0  \n",
      "39             0  \n",
      "90             0  \n",
      "102            0  \n",
      "156            0  \n",
      "182            0  \n",
      "259            0  \n",
      "342            0  \n",
      "362            0  \n",
      "371            0  \n",
      "392            0  \n",
      "520            0  \n",
      "532            0  \n",
      "533            0  \n",
      "553            0  \n",
      "646            0  \n",
      "705            0  \n",
      "736            0  \n",
      "749            0  \n",
      "778            0  \n",
      "868            0  \n",
      "894            0  \n",
      "897            0  \n",
      "984            0  \n",
      "1000           0  \n",
      "1011           0  \n",
      "1035           0  \n",
      "1045           0  \n",
      "1048           0  \n",
      "1049           0  \n",
      "1090           0  \n",
      "1179           0  \n",
      "1216           0  \n",
      "1218           0  \n",
      "1232           0  \n",
      "1321           0  \n",
      "1412           0  \n"
     ]
    }
   ],
   "source": [
    "print(train_data[['BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinType2','BsmtFinSF1','BsmtFinSF2']][train_data['BsmtQual'].isnull() == True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data.loc[train_data['BsmtQual'].isnull(),'BsmtQual'] = 'None'\n",
    "train_data.loc[train_data['BsmtCond'].isnull(),'BsmtCond'] = 'None'\n",
    "train_data.loc[train_data['BsmtExposure'].isnull(),'BsmtExposure'] = 'None'\n",
    "train_data.loc[train_data['BsmtFinType1'].isnull(),'BsmtFinType1'] = 'None'\n",
    "train_data.loc[train_data['BsmtFinType2'].isnull(),'BsmtFinType2'] = 'None'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5 Electrical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SBrkr    1334\n",
      "FuseA      94\n",
      "FuseF      27\n",
      "FuseP       3\n",
      "Mix         1\n",
      "Name: Electrical, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train_data['Electrical'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 大部分值是SBrkr. 缺失值赋予这个值\n",
    "train_data.loc[train_data['Electrical'].isnull(),'Electrical'] = 'SBrkr'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6 FireplaceQu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     FireplaceQu  Fireplaces\n",
      "0            NaN           0\n",
      "5            NaN           0\n",
      "10           NaN           0\n",
      "12           NaN           0\n",
      "15           NaN           0\n",
      "17           NaN           0\n",
      "18           NaN           0\n",
      "19           NaN           0\n",
      "26           NaN           0\n",
      "29           NaN           0\n",
      "30           NaN           0\n",
      "31           NaN           0\n",
      "32           NaN           0\n",
      "36           NaN           0\n",
      "38           NaN           0\n",
      "39           NaN           0\n",
      "42           NaN           0\n",
      "43           NaN           0\n",
      "44           NaN           0\n",
      "47           NaN           0\n",
      "48           NaN           0\n",
      "49           NaN           0\n",
      "50           NaN           0\n",
      "52           NaN           0\n",
      "56           NaN           0\n",
      "57           NaN           0\n",
      "59           NaN           0\n",
      "60           NaN           0\n",
      "61           NaN           0\n",
      "63           NaN           0\n",
      "...          ...         ...\n",
      "1391         NaN           0\n",
      "1397         NaN           0\n",
      "1398         NaN           0\n",
      "1403         NaN           0\n",
      "1404         NaN           0\n",
      "1406         NaN           0\n",
      "1407         NaN           0\n",
      "1408         NaN           0\n",
      "1410         NaN           0\n",
      "1411         NaN           0\n",
      "1412         NaN           0\n",
      "1416         NaN           0\n",
      "1418         NaN           0\n",
      "1422         NaN           0\n",
      "1425         NaN           0\n",
      "1431         NaN           0\n",
      "1432         NaN           0\n",
      "1436         NaN           0\n",
      "1438         NaN           0\n",
      "1444         NaN           0\n",
      "1445         NaN           0\n",
      "1446         NaN           0\n",
      "1448         NaN           0\n",
      "1449         NaN           0\n",
      "1450         NaN           0\n",
      "1452         NaN           0\n",
      "1453         NaN           0\n",
      "1454         NaN           0\n",
      "1458         NaN           0\n",
      "1459         NaN           0\n",
      "\n",
      "[690 rows x 2 columns]\n",
      "Gd    380\n",
      "TA    313\n",
      "Fa     33\n",
      "Ex     24\n",
      "Po     20\n",
      "Name: FireplaceQu, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train_data[['FireplaceQu','Fireplaces']][train_data['FireplaceQu'].isnull()])\n",
    "print(train_data['FireplaceQu'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data.loc[train_data['FireplaceQu'].isnull(),'FireplaceQu'] = 'None'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7 GarageType GarageYrBlt GarageFinish GarageQual GarageCond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     GarageType  GarageYrBlt GarageFinish GarageQual GarageCond  GarageCars  \\\n",
      "39          NaN          NaN          NaN        NaN        NaN           0   \n",
      "48          NaN          NaN          NaN        NaN        NaN           0   \n",
      "78          NaN          NaN          NaN        NaN        NaN           0   \n",
      "88          NaN          NaN          NaN        NaN        NaN           0   \n",
      "89          NaN          NaN          NaN        NaN        NaN           0   \n",
      "99          NaN          NaN          NaN        NaN        NaN           0   \n",
      "108         NaN          NaN          NaN        NaN        NaN           0   \n",
      "125         NaN          NaN          NaN        NaN        NaN           0   \n",
      "127         NaN          NaN          NaN        NaN        NaN           0   \n",
      "140         NaN          NaN          NaN        NaN        NaN           0   \n",
      "148         NaN          NaN          NaN        NaN        NaN           0   \n",
      "155         NaN          NaN          NaN        NaN        NaN           0   \n",
      "163         NaN          NaN          NaN        NaN        NaN           0   \n",
      "165         NaN          NaN          NaN        NaN        NaN           0   \n",
      "198         NaN          NaN          NaN        NaN        NaN           0   \n",
      "210         NaN          NaN          NaN        NaN        NaN           0   \n",
      "241         NaN          NaN          NaN        NaN        NaN           0   \n",
      "250         NaN          NaN          NaN        NaN        NaN           0   \n",
      "287         NaN          NaN          NaN        NaN        NaN           0   \n",
      "291         NaN          NaN          NaN        NaN        NaN           0   \n",
      "307         NaN          NaN          NaN        NaN        NaN           0   \n",
      "375         NaN          NaN          NaN        NaN        NaN           0   \n",
      "386         NaN          NaN          NaN        NaN        NaN           0   \n",
      "393         NaN          NaN          NaN        NaN        NaN           0   \n",
      "431         NaN          NaN          NaN        NaN        NaN           0   \n",
      "434         NaN          NaN          NaN        NaN        NaN           0   \n",
      "441         NaN          NaN          NaN        NaN        NaN           0   \n",
      "464         NaN          NaN          NaN        NaN        NaN           0   \n",
      "495         NaN          NaN          NaN        NaN        NaN           0   \n",
      "520         NaN          NaN          NaN        NaN        NaN           0   \n",
      "...         ...          ...          ...        ...        ...         ...   \n",
      "954         NaN          NaN          NaN        NaN        NaN           0   \n",
      "960         NaN          NaN          NaN        NaN        NaN           0   \n",
      "968         NaN          NaN          NaN        NaN        NaN           0   \n",
      "970         NaN          NaN          NaN        NaN        NaN           0   \n",
      "976         NaN          NaN          NaN        NaN        NaN           0   \n",
      "1009        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1011        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1030        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1038        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1096        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1123        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1131        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1137        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1143        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1173        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1179        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1218        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1219        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1234        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1257        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1283        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1323        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1325        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1326        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1337        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1349        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1407        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1449        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1450        NaN          NaN          NaN        NaN        NaN           0   \n",
      "1453        NaN          NaN          NaN        NaN        NaN           0   \n",
      "\n",
      "      GarageArea  \n",
      "39             0  \n",
      "48             0  \n",
      "78             0  \n",
      "88             0  \n",
      "89             0  \n",
      "99             0  \n",
      "108            0  \n",
      "125            0  \n",
      "127            0  \n",
      "140            0  \n",
      "148            0  \n",
      "155            0  \n",
      "163            0  \n",
      "165            0  \n",
      "198            0  \n",
      "210            0  \n",
      "241            0  \n",
      "250            0  \n",
      "287            0  \n",
      "291            0  \n",
      "307            0  \n",
      "375            0  \n",
      "386            0  \n",
      "393            0  \n",
      "431            0  \n",
      "434            0  \n",
      "441            0  \n",
      "464            0  \n",
      "495            0  \n",
      "520            0  \n",
      "...          ...  \n",
      "954            0  \n",
      "960            0  \n",
      "968            0  \n",
      "970            0  \n",
      "976            0  \n",
      "1009           0  \n",
      "1011           0  \n",
      "1030           0  \n",
      "1038           0  \n",
      "1096           0  \n",
      "1123           0  \n",
      "1131           0  \n",
      "1137           0  \n",
      "1143           0  \n",
      "1173           0  \n",
      "1179           0  \n",
      "1218           0  \n",
      "1219           0  \n",
      "1234           0  \n",
      "1257           0  \n",
      "1283           0  \n",
      "1323           0  \n",
      "1325           0  \n",
      "1326           0  \n",
      "1337           0  \n",
      "1349           0  \n",
      "1407           0  \n",
      "1449           0  \n",
      "1450           0  \n",
      "1453           0  \n",
      "\n",
      "[81 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "print(train_data[['GarageType','GarageYrBlt','GarageFinish','GarageQual','GarageCond','GarageCars','GarageArea']][train_data['GarageType'].isnull() == True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data.loc[train_data['GarageType'].isnull(),'GarageType'] = 'None'\n",
    "train_data.loc[train_data['GarageYrBlt'].isnull(),'GarageYrBlt'] = 'None'\n",
    "train_data.loc[train_data['GarageFinish'].isnull(),'GarageFinish'] = 'None'\n",
    "train_data.loc[train_data['GarageQual'].isnull(),'GarageQual'] = 'None'\n",
    "train_data.loc[train_data['GarageCond'].isnull(),'GarageCond'] = 'None'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8 PoolQC "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gd    3\n",
      "Fa    2\n",
      "Ex    2\n",
      "Name: PoolQC, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train_data['PoolQC'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data.loc[train_data['PoolQC'].isnull(),'PoolQC'] = 'None'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9 Fence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MnPrv    157\n",
      "GdPrv     59\n",
      "GdWo      54\n",
      "MnWw      11\n",
      "Name: Fence, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train_data['Fence'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data.loc[train_data['Fence'].isnull(),'Fence'] = 'None'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None     1179\n",
      "MnPrv     157\n",
      "GdPrv      59\n",
      "GdWo       54\n",
      "MnWw       11\n",
      "Name: Fence, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train_data['Fence'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10 MiscFeature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shed    49\n",
      "Gar2     2\n",
      "Othr     2\n",
      "TenC     1\n",
      "Name: MiscFeature, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train_data['MiscFeature'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data.loc[train_data['MiscFeature'].isnull(),'MiscFeature'] = 'None'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None    1406\n",
      "Shed      49\n",
      "Gar2       2\n",
      "Othr       2\n",
      "TenC       1\n",
      "Name: MiscFeature, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train_data['MiscFeature'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "#确定已经没有缺失值\n",
    "missing = train_data.columns[train_data.isnull().any()].tolist()\n",
    "print(train_data[missing].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 特征变量之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ypWo1X1xcTNzeOZR1qw9c1b7HjybQpFkNTCYjbu6uBIcEsHH9YafHuPN4CtX9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203fd9eada0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data_core = train_data.corr().abs()\n",
    "top_corr_features = train_data_core.index[abs(train_data_core[\"SalePrice\"])>0.5]\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(train_data[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看SalePrice的分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T0WeLi8UTO2/cjCukytO7GinMSOa8ihyn4xgHVOSl8R+3reRjP9vEZ365hWuW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\nK+7GGJOA/j+BXnEgYWjoIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203f8da8550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['SalePrice_log'] = np.log(train_data['SalePrice'])\n",
    "sns.distplot(train_data['SalePrice_log'])\n",
    "plt.show()\n",
    "train_data.drop(['SalePrice_log'],axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j9bGRzFxMmQlhzpw5PRUqSRP3jgP/W9spqGXVbqOVkWHnATdl5sc7di0DhkeULQAu7Yif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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203fdc57080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SalePrice和OverallQual的关系图\n",
    "sns.barplot(train_data['OverallQual'],train_data['SalePrice'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 异常值的处理\n",
    "#### SalePrice 与 GrLiveArea之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mY+utk9Zpq5pnMKlNIxn05L7+NWzjLsJS2BP2JiYmQtddWFjQE/Ikln4QNKag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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203fdaf1550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y =train_data.SalePrice,x = train_data.GrLivArea,c = 'black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SalePrice与TotalBsmtSF的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vT+NOZaRGWnqoqarIhVrdeqrtN/lmp0aDRtYNLUvtP1RWYIiiqO7kS0VNKx1r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\no2ff+TkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x203fdc225c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y =train_data.SalePrice,x = train_data.TotalBsmtSF,c = 'black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 从散点图可以看出，有几个异常值。GrLivArea: 地上居住面积 和 TotalBsmtSF: 地下室总面积很大时，价格却很低。个人认为这些值有异常，先从数据集中删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data = train_data.drop(train_data[(train_data['GrLivArea'] > 4000) & (train_data['SalePrice'] < 300000)].index)\n",
    "train_data = train_data.drop(train_data[(train_data['TotalBsmtSF'] > 6000) & (train_data['SalePrice'] < 300000)].index)\n",
    "\n",
    "tmp_data = train_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 特征工程\n",
    "#### 2.1 类别编码 有些特征是有顺序的，比如质量类别、销售年月、条件类别等，编码后需要保证之前的顺序关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data['MSSubClass'] = train_data['MSSubClass'].apply(str)\n",
    "train_data['OverallQual'] = train_data['OverallQual'].apply(str)\n",
    "train_data['OverallCond'] = train_data['OverallCond'].apply(str)\n",
    "train_data['YrSold'] = train_data['YrSold'].astype(str)\n",
    "train_data['MoSold'] = train_data['MoSold'].astype(str)\n",
    "train_data['GarageYrBlt'] = train_data['GarageYrBlt'].astype(str)\n",
    "train_data['YearBuilt'] = train_data['YearBuilt'].astype(str)\n",
    "\n",
    "\n",
    "class_cols = ('MSSubClass','Street', 'Alley','OverallQual','OverallCond','BsmtQual', 'BsmtCond','BsmtFinType1', \n",
    "              'BsmtFinType2','GarageQual', 'GarageCond','ExterQual', 'ExterCond','HeatingQC','KitchenQual',\n",
    "              'Functional', 'FireplaceQu', 'Fence','PoolQC', 'GarageYrBlt','GarageFinish', 'LandSlope',\n",
    "              'CentralAir','YrSold', 'MoSold','YearBuilt','YearRemodAdd','SaleCondition')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lb = LabelEncoder()\n",
    "for col_ in class_cols:\n",
    "    train_data[col_] = lb.fit_transform(train_data[col_])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 One-Hot  编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Id  MSSubClass  LotFrontage  LotArea  Street  Alley  LandSlope  \\\n",
      "0   1           9         65.0     8450       1      1          0   \n",
      "1   2           4         80.0     9600       1      1          0   \n",
      "2   3           9         68.0    11250       1      1          0   \n",
      "\n",
      "   OverallQual  OverallCond  YearBuilt       ...        onehotMiscFeature  \\\n",
      "0            7            4        104       ...                        0   \n",
      "1            6            7         77       ...                        0   \n",
      "2            7            4        102       ...                        0   \n",
      "\n",
      "   onehotSaleType  onehotSaleType  onehotSaleType  onehotSaleType  \\\n",
      "0               0               0               0               0   \n",
      "1               0               0               0               0   \n",
      "2               0               0               0               0   \n",
      "\n",
      "   onehotSaleType  onehotSaleType  onehotSaleType  onehotSaleType  \\\n",
      "0               0               0               0               0   \n",
      "1               0               0               0               0   \n",
      "2               0               0               0               0   \n",
      "\n",
      "   onehotSaleType  \n",
      "0               1  \n",
      "1               1  \n",
      "2               1  \n",
      "\n",
      "[3 rows x 228 columns]\n"
     ]
    }
   ],
   "source": [
    "one_hot_cols =('MSZoning','LotShape','LandContour','Utilities','LotConfig','Neighborhood','Condition1',\n",
    "               'Condition2','BldgType','HouseStyle','RoofStyle','RoofMatl','Exterior1st','Exterior2nd',\n",
    "               'MasVnrType','Foundation','BsmtExposure','Heating','CentralAir','Electrical','GarageType',\n",
    "               'PavedDrive','MiscFeature','SaleType')\n",
    "\n",
    "for one_cols in one_hot_cols:\n",
    "    tmp = pd.get_dummies(train_data[one_cols])\n",
    "    tmp = tmp.rename(columns =  lambda x: 'onehot' + str(one_cols))\n",
    "    train_data = pd.concat([train_data,tmp],axis = 1)\n",
    "\n",
    "train_data.drop(['MSZoning','LotShape','LandContour','Utilities','LotConfig','Neighborhood','Condition1',\n",
    "               'Condition2','BldgType','HouseStyle','RoofStyle','RoofMatl','Exterior1st','Exterior2nd',\n",
    "               'MasVnrType','Foundation','BsmtExposure','Heating','CentralAir','Electrical','GarageType',\n",
    "               'PavedDrive','MiscFeature','SaleType'],axis = 1,inplace=True)\n",
    "print(train_data.head(3))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 数值型数据标准化处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "std = StandardScaler()\n",
    "std_col = ['LotFrontage','LotArea','MasVnrArea','BsmtFinSF1','BsmtFinSF2','BsmtUnfSF','TotalBsmtSF','1stFlrSF','2ndFlrSF',\n",
    "           'LowQualFinSF','GrLivArea','TotRmsAbvGrd','GarageArea','WoodDeckSF','OpenPorchSF','EnclosedPorch','3SsnPorch',\n",
    "           'ScreenPorch','MiscVal']\n",
    "std_train = train_data[std_col].values\n",
    "std_train = std.fit_transform(std_train)\n",
    "std_train_df = pd.DataFrame(std_train,columns=['LotFrontage_std','LotArea_std','MasVnrArea_std','BsmtFinSF1_std','BsmtFinSF2_std',\n",
    "                                               'BsmtUnfSF_std','TotalBsmtSF_std','1stFlrSF_std','2ndFlrSF_std','LowQualFinSF_std',\n",
    "                                               'GrLivArea_std','TotRmsAbvGrd_std','GarageArea_std','WoodDeckSF_std',\n",
    "                                               'OpenPorchSF_std','EnclosedPorch_std','3SsnPorch_std','ScreenPorch_std',\n",
    "                                               'MiscVal_std'],index = train_data.index)\n",
    "x_train_new = pd.concat((train_data,std_train_df),axis = 1)\n",
    "x_train_new.drop(['LotFrontage','LotArea','MasVnrArea','BsmtFinSF1','BsmtFinSF2','BsmtUnfSF','TotalBsmtSF','1stFlrSF','2ndFlrSF',\n",
    "           'LowQualFinSF','GrLivArea','TotRmsAbvGrd','GarageArea','WoodDeckSF','OpenPorchSF','EnclosedPorch','3SsnPorch',\n",
    "           'ScreenPorch','MiscVal'], axis = 1,inplace=True)\n",
    "train_data = x_train_new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 保存处理结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop(['Id'],axis=1,inplace=True)\n",
    "train_data.to_csv('final_train_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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